글로벌 광업 운영사. Caterpillar 777 차량 성능을 위한 데이터 과학 최적화.
당사는 GPS 원격 측정, 공회전 이벤트 및 연료 패턴 분석을 통해 Caterpillar 777 운반 트럭 차량의 성능을 최적화했습니다. 해당 작업은 주유소에서의 과도한 공회전 시간과 최적화되지 않은 경로로 인해 어려움을 겪었습니다. ML 기반 예측, 최적화된 속도 구간, 부분 연료 공급으로 전환하여 유효 운반 주기를 3~7% 늘리고 연료 공급 이벤트당 가동 중지 시간을 15~25분 단축했으며, 자본 지출은 전혀 발생하지 않았습니다.
01 —The Impact
The results, up front.
3–7% increase in effective haul cycles 15–25 minutes less downtime per fueling event Annual savings and productivity impact of $2M–$6.2M Achieved entirely via process and analytics — no CAPEX
02 — The Challenge
Where they started.
The operator’s fleet encountered bottlenecks at refueling points and inefficient routing, leading to long idle times and fewer productive hauling cycles. Telemetry data existed but was not utilized to drive operational optimization. We conducted a full analysis of GPS tracks, speed curves, idle events, fuel levels, and historical cycle times. ML models were used to recommend optimal speed corridors, predict cycle time windows, and forecast fuel demand precisely enough to shift away from full-tank fueling. We also introduced high-pressure fueling complexes and developed a DS Advisor tool to assist dispatchers with real-time routing and fueling decisions. The project delivered a step-change in efficiency, all through process excellence and data-backed decisioning — requiring no new CAPEX.
04 — Approach & Methodology
How we got there.
The data revealed that full-tank fueling drastically increased refueling downtime and that trucks often queued unnecessarily. Predictive fuel-demand modeling enabled partial fueling at optimal times, which significantly decreased wait time and increased cycle availability.
Routing adjustments based on recommended speed corridors further improved predictability and reduced cycle-time variance.
05 —In Practice
Project samples.
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